Abstract
We present a general framework for combination of two distinct local denoising methods. Interpolation between the two methods is controlled by a spatially varying decision function. Assuming the availability of clean training data, we formulate a learning problem for determining the decision function. As an example application we use Weighted Kernel Ridge Regression to solve this learning problem for a pair of wavelet-based image denoising algorithms, yielding a "hybrid" denoising algorithm whose performance surpasses that of either initial method.
Keywords
Noise reductionComputer scienceArtificial intelligenceKernel (algebra)WaveletMachine learningFunction (biology)Pattern recognition (psychology)Image denoisingMathematics
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Publication Info
- Year
- 2007
- Type
- article
- Volume
- 24
- Pages
- VI - 29
- Citations
- 11
- Access
- Closed
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Cite This
David K. Hammond,
Eero P. Simoncelli
(2007).
A Machine Learning Framework for Adaptive Combination of Signal Denoising Methods.
, 24
, VI - 29.
https://doi.org/10.1109/icip.2007.4379513
Identifiers
- DOI
- 10.1109/icip.2007.4379513